Band 10535
Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II
Aus der Reihe
Lecture Notes in Artificial Intelligence
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- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
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Verlag:Springer
- Springer Fr. 73.90 ausgewählt
- Springer Berlin Fr. 73.90
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Auflage:1st edition 2017
Fr. 73.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
10.01.2018
Abbildungen
XXXIII, 213 illus., schwarz-weiss Illustrationen
Herausgeber
Michelangelo Ceci + weitereVerlag
SpringerSeitenzahl
866
Maße (L/B/H)
23.5/15.5/4.8 cm
Gewicht
1334 g
Auflage
1st edition 2017
Sprache
Englisch
ISBN
978-3-319-71245-1
The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.
The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.
The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.
The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.
The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.
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